{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
    "<div class=\"align-center\">\n",
    "<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
    "<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + \u2b50 <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\n",
    "</div>\n",
    "\n",
    "To install Unsloth your local device, follow [our guide](https://docs.unsloth.ai/get-started/install-and-update). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\n",
    "\n",
    "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### News"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Introducing FP8 precision training for faster RL inference. [Read Blog](https://docs.unsloth.ai/new/fp8-reinforcement-learning).\n",
    "\n",
    "Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker).\n",
    "\n",
    "[gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels!\n",
    "\n",
    "Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM.\n",
    "\n",
    "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "%%capture\nimport os, re\nif \"COLAB_\" not in \"\".join(os.environ.keys()):\n    !pip install unsloth\nelse:\n    # Do this only in Colab notebooks! Otherwise use pip install unsloth\n    import torch; v = re.match(r\"[0-9]{1,}\\.[0-9]{1,}\", str(torch.__version__)).group(0)\n    xformers = \"xformers==\" + (\"0.0.33.post1\" if v==\"2.9\" else \"0.0.32.post2\" if v==\"2.8\" else \"0.0.29.post3\")\n    !pip install --no-deps bitsandbytes accelerate {xformers} peft trl triton cut_cross_entropy unsloth_zoo\n    !pip install sentencepiece protobuf \"datasets==4.3.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n    !pip install --no-deps unsloth\n!pip install transformers==4.56.2\n!pip install --no-deps trl==0.22.2"
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GFOEZbP7ONMs"
   },
   "source": [
    "### Unsloth"
   ]
  },
  {
   "cell_type": "code",
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    "id": "QmUBVEnvCDJv",
    "outputId": "7b2cc094-d9e1-4dc3-da42-6c22eef471da"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
      "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n",
      "==((====))==  Unsloth 2025.6.6: Fast Gemma3 patching. Transformers: 4.52.4.\n",
      "   \\\\   /|    Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n",
      "O^O/ \\_/ \\    Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0\n",
      "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. FA2 = False]\n",
      " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
      "Unsloth: Using float16 precision for gemma3 won't work! Using float32.\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "model.safetensors:   0%|          | 0.00/4.38G [00:00<?, ?B/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n"
     ]
    },
    {
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       "tokenizer_config.json: 0.00B [00:00, ?B/s]"
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       "tokenizer.model:   0%|          | 0.00/4.69M [00:00<?, ?B/s]"
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    {
     "data": {
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    },
    {
     "data": {
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   ],
   "source": [
    "from unsloth import FastVisionModel # FastLanguageModel for LLMs\n",
    "import torch\n",
    "\n",
    "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
    "fourbit_models = [\n",
    "    \"unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit\", # Llama 3.2 vision support\n",
    "    \"unsloth/Llama-3.2-11B-Vision-bnb-4bit\",\n",
    "    \"unsloth/Llama-3.2-90B-Vision-Instruct-bnb-4bit\", # Can fit in a 80GB card!\n",
    "    \"unsloth/Llama-3.2-90B-Vision-bnb-4bit\",\n",
    "\n",
    "    \"unsloth/Pixtral-12B-2409-bnb-4bit\",              # Pixtral fits in 16GB!\n",
    "    \"unsloth/Pixtral-12B-Base-2409-bnb-4bit\",         # Pixtral base model\n",
    "\n",
    "    \"unsloth/Qwen2-VL-2B-Instruct-bnb-4bit\",          # Qwen2 VL support\n",
    "    \"unsloth/Qwen2-VL-7B-Instruct-bnb-4bit\",\n",
    "    \"unsloth/Qwen2-VL-72B-Instruct-bnb-4bit\",\n",
    "\n",
    "    \"unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit\",      # Any Llava variant works!\n",
    "    \"unsloth/llava-1.5-7b-hf-bnb-4bit\",\n",
    "] # More models at https://huggingface.co/unsloth\n",
    "\n",
    "model, processor = FastVisionModel.from_pretrained(\n",
    "    \"unsloth/gemma-3-4b-pt\",\n",
    "    load_in_4bit = True, # Use 4bit to reduce memory use. False for 16bit LoRA.\n",
    "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for long context\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SXd9bTZd1aaL"
   },
   "source": [
    "We now add LoRA adapters for parameter efficient fine-tuning, allowing us to train only 1% of all model parameters efficiently.\n",
    "\n",
    "**[NEW]** We also support fine-tuning only the vision component, only the language component, or both. Additionally, you can choose to fine-tune the attention modules, the MLP layers, or both!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6bZsfBuZDeCL",
    "outputId": "2f629d76-5953-4946-b6a9-9dd1e8ca6939"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unsloth: Making `base_model.model.model.vision_tower.vision_model` require gradients\n"
     ]
    }
   ],
   "source": [
    "model = FastVisionModel.get_peft_model(\n",
    "    model,\n",
    "    finetune_vision_layers     = True, # False if not finetuning vision layers\n",
    "    finetune_language_layers   = True, # False if not finetuning language layers\n",
    "    finetune_attention_modules = True, # False if not finetuning attention layers\n",
    "    finetune_mlp_modules       = True, # False if not finetuning MLP layers\n",
    "\n",
    "    r = 16,                           # The larger, the higher the accuracy, but might overfit\n",
    "    lora_alpha = 16,                  # Recommended alpha == r at least\n",
    "    lora_dropout = 0,\n",
    "    bias = \"none\",\n",
    "    random_state = 3407,\n",
    "    use_rslora = False,               # We support rank stabilized LoRA\n",
    "    loftq_config = None,               # And LoftQ\n",
    "    target_modules = \"all-linear\",    # Optional now! Can specify a list if needed\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vITh0KVJ10qX"
   },
   "source": [
    "<a name=\"Data\"></a>\n",
    "### Data Prep\n",
    "We'll use a sampled dataset of handwritten math formulas. The objective is to convert these images into a computer-readable format\u2014specifically LaTeX\u2014so they can be rendered. This is particularly useful for complex expressions.\n",
    "\n",
    "You can access the dataset [here](https://huggingface.co/datasets/unsloth/LaTeX_OCR). The full dataset is [here](https://huggingface.co/datasets/linxy/LaTeX_OCR)."
   ]
  },
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      "f57e983b895c4939872e0e35aceaa1ad",
      "aef9037557774aeda772ec354fd69783",
      "fd7000baed8346e98de4abfbca851689",
      "ff1c1da4242e47f3b2a5eb6f16f90fa5",
      "cf4733939e8848feb3fc7c3c6709cfd6",
      "eab194e82f854ffb82adcd5d0705c4cc",
      "858fecbe71514cd983ee0358a3dadfbc"
     ]
    },
    "id": "LjY75GoYUCB8",
    "outputId": "651a35d0-7bd8-4094-858d-17cec302e330"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "900e2c0047b54a429c4f93c311037380",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/519 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0fb0ae113ae448108d6a3ea54da95d62",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "data/train-00000-of-00001.parquet:   0%|          | 0.00/344M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ceaf4e478f5474a806323116e1a3779",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "data/test-00000-of-00001.parquet:   0%|          | 0.00/38.2M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c631fb112cb64f6abacc18ae5e24ee8b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/68686 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2ebdae870ee94f0eac6113cccffff29e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/7632 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "dataset = load_dataset(\"unsloth/LaTeX_OCR\", split = \"train\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W1W2Qhsz6rUT"
   },
   "source": [
    "Let's take an overview of the dataset. We'll examine the second image and its corresponding caption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bfcSGwIb6p_R",
    "outputId": "5973c7b6-3189-41c4-d65f-18f1622e5d00"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['image', 'text'],\n",
       "    num_rows: 68686\n",
       "})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "id": "uOLWY2936t1n",
    "outputId": "4ee679c7-9b1f-4454-a3fd-aa4d74e00294"
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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FFAH/2Q==",
      "image/png": "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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=320x50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[2][\"image\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "lXjfJr4W6z8P",
    "outputId": "ddb56f54-110c-41f8-e324-f9ba60039794"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'H ^ { \\\\prime } = \\\\beta N \\\\int d \\\\lambda \\\\biggl \\\\{ \\\\frac { 1 } { 2 \\\\beta ^ { 2 } N ^ { 2 } } \\\\partial _ { \\\\lambda } \\\\zeta ^ { \\\\dagger } \\\\partial _ { \\\\lambda } \\\\zeta + V ( \\\\lambda ) \\\\zeta ^ { \\\\dagger } \\\\zeta \\\\biggr \\\\} \\\\ .'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[2][\"text\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rKHxfZua1CrS"
   },
   "source": [
    "We can also render LaTeX directly in the browser!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "id": "nPopsxAC1CrS",
    "outputId": "ecd52dfb-2ffe-4064-9bb1-8f86664684bd"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\sigma ^ { \\mu } \\frac { \\lambda ^ { a } } { 2 } A _ { \\mu } ^ { a } .$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Math, Latex\n",
    "\n",
    "latex = dataset[3][\"text\"]\n",
    "display(Math(latex))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K9CBpiISFa6C"
   },
   "source": [
    "To format the dataset, all vision fine-tuning tasks should follow this format:\n",
    "\n",
    "```python\n",
    "[\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\"type\": \"text\", \"text\": instruction},\n",
    "            {\"type\": \"image\", \"image\": sample[\"image\"]},\n",
    "        ],\n",
    "    },\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\"type\": \"text\", \"text\": instruction},\n",
    "            {\"type\": \"image\", \"image\": sample[\"image\"]},\n",
    "        ],\n",
    "    },\n",
    "]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "oPXzJZzHEgXe"
   },
   "outputs": [],
   "source": [
    "instruction = \"Write the LaTeX representation for this image.\"\n",
    "\n",
    "def convert_to_conversation(sample):\n",
    "    conversation = [\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": [\n",
    "                {\"type\": \"text\", \"text\": instruction},\n",
    "                {\"type\": \"image\", \"image\": sample[\"image\"]},\n",
    "            ],\n",
    "        },\n",
    "        {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": sample[\"text\"]}]},\n",
    "    ]\n",
    "    return {\"messages\": conversation}\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FY-9u-OD6_gE"
   },
   "source": [
    "Let's convert the dataset into the \"correct\" format for finetuning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "gFW2qXIr7Ezy"
   },
   "outputs": [],
   "source": [
    "converted_dataset = [convert_to_conversation(sample) for sample in dataset]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ndDUB23CGAC5"
   },
   "source": [
    "The first example is now structured like below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gGFzmplrEy9I",
    "outputId": "da82ca66-a9c0-44d7-a7c0-8d64a5b56612"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'messages': [{'role': 'user',\n",
       "   'content': [{'type': 'text',\n",
       "     'text': 'Write the LaTeX representation for this image.'},\n",
       "    {'type': 'image',\n",
       "     'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=160x40>}]},\n",
       "  {'role': 'assistant',\n",
       "   'content': [{'type': 'text',\n",
       "     'text': '{ \\\\frac { N } { M } } \\\\in { \\\\bf Z } , { \\\\frac { M } { P } } \\\\in { \\\\bf Z } , { \\\\frac { P } { Q } } \\\\in { \\\\bf Z }'}]}]}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "converted_dataset[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GS55FC1f1CrS"
   },
   "source": [
    "Lets take the Gemma 3 instruction chat template and use it in our base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "bEzvL7Sm1CrS"
   },
   "outputs": [],
   "source": [
    "from unsloth import get_chat_template\n",
    "\n",
    "processor = get_chat_template(\n",
    "    processor,\n",
    "    \"gemma-3\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FecKS-dA82f5"
   },
   "source": [
    "Before fine-tuning, let us evaluate the base model's performance. We do not expect strong results, as it has not encountered this chat template before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vcat4UxA81vr",
    "outputId": "00dc0ddb-b981-4d90-c114-338ca6b69b2c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You have set `compile_config`, but we are unable to meet the criteria for compilation. Compilation will be skipped.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model\n",
      "model\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "<start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image><start_of_image>]](]\n",
      ")]\n",
      "]\n",
      "]]\n",
      "Write the LaTeX representation for this image.\n",
      ".\n",
      ".\n",
      ".\n",
      "Write the LaTeX representation for this image.\n",
      "Write the LaTeX representation for this image.\n",
      "Write the LaTeX representation for this image.\n",
      "Write the LaTeX representation for this image.\n",
      "Write the LaTeX representation for this image.\n",
      "Write the LaTeX representation for this image.\n"
     ]
    }
   ],
   "source": [
    "FastVisionModel.for_inference(model)  # Enable for inference!\n",
    "\n",
    "image = dataset[2][\"image\"]\n",
    "instruction = \"Write the LaTeX representation for this image.\"\n",
    "\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": instruction}],\n",
    "    }\n",
    "]\n",
    "input_text = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
    "inputs = processor(\n",
    "    image,\n",
    "    input_text,\n",
    "    add_special_tokens=False,\n",
    "    return_tensors=\"pt\",\n",
    ").to(\"cuda\")\n",
    "\n",
    "from transformers import TextStreamer\n",
    "\n",
    "text_streamer = TextStreamer(processor, skip_prompt=True)\n",
    "result = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n",
    "                        use_cache=True, temperature = 1.0, top_p = 0.95, top_k = 64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FeAiMlQ71CrS"
   },
   "source": [
    "You can see it's absolutely terrible! It doesn't follow instructions at all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "idAEIeSQ3xdS"
   },
   "source": [
    "<a name=\"Train\"></a>\n",
    "### Train the model\n",
    "Now let's train our model. We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!\n",
    "\n",
    "We use our new `UnslothVisionDataCollator` which will help in our vision finetuning setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "95_Nn-89DhsL",
    "outputId": "78c74c51-f328-485d-fdb7-3bab581e8768"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unsloth: Switching to float32 training since model cannot work with float16\n"
     ]
    }
   ],
   "source": [
    "from unsloth.trainer import UnslothVisionDataCollator\n",
    "from trl import SFTTrainer, SFTConfig\n",
    "\n",
    "FastVisionModel.for_training(model) # Enable for training!\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=converted_dataset,\n",
    "    processing_class=processor.tokenizer,\n",
    "    data_collator=UnslothVisionDataCollator(model, processor),\n",
    "    args = SFTConfig(\n",
    "        per_device_train_batch_size = 1,\n",
    "        gradient_accumulation_steps = 4,\n",
    "        gradient_checkpointing = True,\n",
    "\n",
    "        # use reentrant checkpointing\n",
    "        gradient_checkpointing_kwargs = {\"use_reentrant\": False},\n",
    "        max_grad_norm = 0.3,              # max gradient norm based on QLoRA paper\n",
    "        warmup_ratio = 0.03,\n",
    "        max_steps = 30,\n",
    "        #num_train_epochs = 2,          # Set this instead of max_steps for full training runs\n",
    "        learning_rate = 2e-4,\n",
    "        logging_steps = 1,\n",
    "        save_strategy=\"steps\",\n",
    "        optim = \"adamw_torch_fused\",\n",
    "        weight_decay = 0.001,\n",
    "        lr_scheduler_type = \"cosine\",\n",
    "        seed = 3407,\n",
    "        output_dir = \"outputs\",\n",
    "        report_to = \"none\",             # For Weights and Biases\n",
    "\n",
    "        # You MUST put the below items for vision finetuning:\n",
    "        remove_unused_columns = False,\n",
    "        dataset_text_field = \"\",\n",
    "        dataset_kwargs = {\"skip_prepare_dataset\": True},\n",
    "        max_length = 2048,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2ejIt2xSNKKp",
    "outputId": "e9119560-5845-42ac-a05e-d5dfd067472b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU = Tesla T4. Max memory = 14.741 GB.\n",
      "5.416 GB of memory reserved.\n"
     ]
    }
   ],
   "source": [
    "# @title Show current memory stats\n",
    "gpu_stats = torch.cuda.get_device_properties(0)\n",
    "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
    "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
    "print(f\"{start_gpu_memory} GB of memory reserved.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "yqxqAZ7KJ4oL",
    "outputId": "fde31212-3649-4fbd-82b5-c8f355335333"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
      "   \\\\   /|    Num examples = 68,686 | Num Epochs = 1 | Total steps = 30\n",
      "O^O/ \\_/ \\    Batch size per device = 1 | Gradient accumulation steps = 4\n",
      "\\        /    Data Parallel GPUs = 1 | Total batch size (1 x 4 x 1) = 4\n",
      " \"-____-\"     Trainable parameters = 38,497,792/4,000,000,000 (0.96% trained)\n",
      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [30/30 10:11, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.479900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.098400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.089700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.224700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.667500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.509600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.855900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.664800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.566600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.221500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.395000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.269400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.413900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.286000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.407300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.317800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.270100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.246500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.257500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.238000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.303100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.263000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.383700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.249300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.191900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.295800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.122200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.239600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.338300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.270500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer_stats = trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pCqnaKmlO1U9",
    "outputId": "c07d7c52-f5cd-4f9d-fb43-96c20bca110d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "760.6053 seconds used for training.\n",
      "12.68 minutes used for training.\n",
      "Peak reserved memory = 6.061 GB.\n",
      "Peak reserved memory for training = 0.645 GB.\n",
      "Peak reserved memory % of max memory = 41.117 %.\n",
      "Peak reserved memory for training % of max memory = 4.376 %.\n"
     ]
    }
   ],
   "source": [
    "# @title Show final memory and time stats\n",
    "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
    "used_percentage = round(used_memory / max_memory * 100, 3)\n",
    "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n",
    "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
    "print(\n",
    "    f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n",
    ")\n",
    "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
    "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
    "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
    "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ekOmTR1hSNcr"
   },
   "source": [
    "<a name=\"Inference\"></a>\n",
    "### Inference\n",
    "Let's run the model! You can modify the instruction and input\u2014just leave the output blank.\n",
    "\n",
    "We'll use the best hyperparameters for inference on Gemma: `top_p=0.95`, `top_k=64`, and `temperature=1.0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kR3gIAX-SM2q",
    "outputId": "4007fc41-1c37-4f9c-a36b-bb49470f3fd5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "\\left( { B _ { n } ^ { + } , { q } _ { 2 } , { k } _ { 2 } ^ { + } \\right)\n",
      "[\n",
      "\\left. { { q } _ { 2 } , k _ { 2 } ^ { + } { k } _ { 2 } ^ { + } , { q } _ { 2 } ^ { + } } \\right]\n",
      "= n B _ { n } ^ { + } , { q _ { 2 } }\n",
      "image<end_of_turn>\n"
     ]
    }
   ],
   "source": [
    "FastVisionModel.for_inference(model)  # Enable for inference!\n",
    "\n",
    "image = dataset[10][\"image\"]\n",
    "instruction = \"Write the LaTeX representation for this image.\"\n",
    "\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": instruction}],\n",
    "    }\n",
    "]\n",
    "\n",
    "input_text = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
    "inputs = processor(\n",
    "    image,\n",
    "    input_text,\n",
    "    add_special_tokens=False,\n",
    "    return_tensors=\"pt\",\n",
    ").to(\"cuda\")\n",
    "\n",
    "from transformers import TextStreamer\n",
    "\n",
    "text_streamer = TextStreamer(processor, skip_prompt=True)\n",
    "result = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n",
    "                        use_cache=True, temperature = 1.0, top_p = 0.95, top_k = 64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uMuVrWbjAzhc"
   },
   "source": [
    "<a name=\"Save\"></a>\n",
    "### Saving, loading finetuned models\n",
    "To save the final model as LoRA adapters, use Hugging Face\u2019s `push_to_hub` for online saving, or `save_pretrained` for local storage.\n",
    "\n",
    "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "upcOlWe7A1vc",
    "outputId": "5d6d63e2-0e87-4abb-c5ff-dd183aeff3c1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lora_model/processor_config.json']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.save_pretrained(\"lora_model\")  # Local saving\n",
    "processor.save_pretrained(\"lora_model\")\n",
    "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
    "# processor.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AEEcJ4qfC7Lp"
   },
   "source": [
    "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MKX_XKs_BNZR",
    "outputId": "57e825f2-ce09-4322-e48a-3c82ef92dfda"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A _ { \\mu } ^ { \\alpha \\beta } \\bar { A } _ { \\mu } ^ { \\alpha \\beta } = 0 ,<end_of_turn>\n"
     ]
    }
   ],
   "source": [
    "if False:\n",
    "    from unsloth import FastVisionModel\n",
    "\n",
    "    model, processor = FastVisionModel.from_pretrained(\n",
    "        model_name=\"lora_model\",  # YOUR MODEL YOU USED FOR TRAINING\n",
    "        load_in_4bit=True,  # Set to False for 16bit LoRA\n",
    "    )\n",
    "    FastVisionModel.for_inference(model)  # Enable for inference!\n",
    "\n",
    "FastVisionModel.for_inference(model)  # Enable for inference!\n",
    "\n",
    "sample = dataset[1]\n",
    "image = sample[\"image\"].convert(\"RGB\")\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\n",
    "                \"type\": \"text\",\n",
    "                \"text\": sample[\"text\"],\n",
    "            },\n",
    "            {\n",
    "                \"type\": \"image\",\n",
    "            },\n",
    "        ],\n",
    "    },\n",
    "]\n",
    "input_text = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
    "inputs = processor(\n",
    "    image,\n",
    "    input_text,\n",
    "    add_special_tokens=False,\n",
    "    return_tensors=\"pt\",\n",
    ").to(\"cuda\")\n",
    "\n",
    "from transformers import TextStreamer\n",
    "\n",
    "text_streamer = TextStreamer(processor.tokenizer, skip_prompt=True)\n",
    "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n",
    "                   use_cache=True, temperature = 1.0, top_p = 0.95, top_k = 64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f422JgM9sdVT"
   },
   "source": [
    "### Saving to float16 for VLLM\n",
    "\n",
    "We also support saving to `float16` directly. Select `merged_16bit` for float16. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "iHjt_SMYsd3P"
   },
   "outputs": [],
   "source": [
    "# Select ONLY 1 to save! (Both not needed!)\n",
    "\n",
    "# Save locally to 16bit\n",
    "if False: model.save_pretrained_merged(\"unsloth_finetune\", processor,)\n",
    "\n",
    "# To export and save to your Hugging Face account\n",
    "if False: model.push_to_hub_merged(\"YOUR_USERNAME/unsloth_finetune\", processor, token = \"PUT_HERE\")"
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   "source": [
    "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
    "\n",
    "Some other links:\n",
    "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
    "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
    "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
    "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n",
    "\n",
    "<div class=\"align-center\">\n",
    "  <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "  <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
    "  <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
    "\n",
    "  Join Discord if you need help + \u2b50\ufe0f <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\ufe0f\n",
    "\n",
    "  This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme)\n",
    "</div>\n"
   ]
  }
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